Techniques for accomplishing automatic speech recognition (ASR) are well known. Among known ASR techniques are those that use grammars. A grammar is a representation of the language or phrases expected to be used or spoken in a given context. In one sense, then, ASR grammars typically constrain the speech recognizer to a vocabulary that is a subset of the universe of potentially-spoken words; and grammars may include subgrammars. An ASR grammar rule can then be used to represent the set of “phrases” or combinations of words from one or more grammars or subgrammars that may be expected in a given context. “Grammar” may also refer generally to a statistical language model (where a model represents phrases), such as those used in language understanding systems.
Products and services that utilize some form of automatic speech recognition (“ASR”) methodology have been recently introduced commercially. For example, AT&T has developed a grammar-based ASR engine called WATSON that enables development of complex ASR services. Desirable attributes of complex ASR services that would utilize such ASR technology include high accuracy in recognition; robustness to enable recognition where speakers have differing accents or dialects, and/or in the presence of background noise; ability to handle large vocabularies; and natural language understanding. In order to achieve these attributes for complex ASR services, ASR techniques and engines typically require computer-based systems having significant processing capability in order to achieve the desired speech recognition capability. In addition to WATSON, numerous ASR services are available which are typically based on personal computer (PC) technology.
One of the fundamental problem areas in speech and language research, particularly with regards to perception, cognition and artificial intelligence, concerns the adaptive tuning of recognition mechanisms, and the manner in which such tuning can alter the groupings which emerge within a context of familiar elements. Adaptive tuning of recognition processes is one of the mechanisms whereby representations become unitized or chunked into coherent recognition codes through experience.
Packet networks are general-purpose data networks which are well-suited for sending stored data of various types, including speech or audio. The Internet, the largest and most renowned of the existing packet networks, connects over 4 million computers in some 140 countries. The Internet's global and exponential growth is common knowledge today.
Typically, one accesses a packet network, such as the Internet, through a client software program executing on a computer, such as a PC, and so packet networks are inherently client/server oriented. One way of accessing information over a packet network is through use of a Web browser (such as the Netscape Navigator, available from Netscape Communications, Inc., and the Internet Explorer, available from Microsoft Corp.) which enables a client to interact with Web servers. Web servers and the information available therein are typically identified and addressed through a Uniform Resource Locator (URL)-compatible address. URL addressing is widely used in Internet and intranet applications and is well known to those skilled in the art (an “intranet” is a packet network modeled in functionality based upon the Internet and is used, e.g., by companies locally or internally).
There is a thus a need to utilize a packet switched network such as the Internet to overcome some of the problems with speech recognition.